Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition
Summary: Recognizing real-world scenes requires integrating sensory (likelihood) and prior information, yet how the brain represents these components remains unclear. To investigate this, we employed deep image transformation to generate images with parametrically controlled naturalness, enabling pr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225009241 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849762588208922624 |
|---|---|
| author | Kojiro Hayashi Risa Katayama Keisuke Fujimoto Wako Yoshida Shin Ishii |
| author_facet | Kojiro Hayashi Risa Katayama Keisuke Fujimoto Wako Yoshida Shin Ishii |
| author_sort | Kojiro Hayashi |
| collection | DOAJ |
| description | Summary: Recognizing real-world scenes requires integrating sensory (likelihood) and prior information, yet how the brain represents these components remains unclear. To investigate this, we employed deep image transformation to generate images with parametrically controlled naturalness, enabling precise manipulation of likelihood uncertainty. Concurrently, we designed a sequential image-scene recognition task that quantitatively modulates prior information. By combining these AI-generated images with the task, we conducted a functional magnetic resonance imaging (fMRI) experiment enabling systematic control of both likelihood and prior information. The results revealed that higher visual areas were activated when viewing images with low likelihood uncertainty. In contrast, the default mode network, which includes the medial prefrontal gyrus, inferior parietal lobule, and middle temporal gyrus, exhibited higher activation when more prior information was available. This approach highlights how applying AI technology to neuroscience questions can enhance our understanding of neural mechanisms underlying scene recognition. |
| format | Article |
| id | doaj-art-cc844ea7e13b4e7dbe1080afad23a3dd |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-cc844ea7e13b4e7dbe1080afad23a3dd2025-08-20T03:05:42ZengElsevieriScience2589-00422025-06-0128611266310.1016/j.isci.2025.112663Neural mechanisms in resolving prior and likelihood uncertainty in scene recognitionKojiro Hayashi0Risa Katayama1Keisuke Fujimoto2Wako Yoshida3Shin Ishii4Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan; Corresponding authorGraduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto 619-0288, JapanGraduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto 619-0288, JapanNuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK; Department of Neural Computation for Decision-making, Advanced Telecommunications Research Institute International, Kyoto 619-0288, JapanGraduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan; International Research Center for Neurointelligence, the University of Tokyo, Tokyo 113-0033, JapanSummary: Recognizing real-world scenes requires integrating sensory (likelihood) and prior information, yet how the brain represents these components remains unclear. To investigate this, we employed deep image transformation to generate images with parametrically controlled naturalness, enabling precise manipulation of likelihood uncertainty. Concurrently, we designed a sequential image-scene recognition task that quantitatively modulates prior information. By combining these AI-generated images with the task, we conducted a functional magnetic resonance imaging (fMRI) experiment enabling systematic control of both likelihood and prior information. The results revealed that higher visual areas were activated when viewing images with low likelihood uncertainty. In contrast, the default mode network, which includes the medial prefrontal gyrus, inferior parietal lobule, and middle temporal gyrus, exhibited higher activation when more prior information was available. This approach highlights how applying AI technology to neuroscience questions can enhance our understanding of neural mechanisms underlying scene recognition.http://www.sciencedirect.com/science/article/pii/S2589004225009241NeuroscienceSensory neuroscienceCognitive neuroscience |
| spellingShingle | Kojiro Hayashi Risa Katayama Keisuke Fujimoto Wako Yoshida Shin Ishii Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition iScience Neuroscience Sensory neuroscience Cognitive neuroscience |
| title | Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| title_full | Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| title_fullStr | Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| title_full_unstemmed | Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| title_short | Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| title_sort | neural mechanisms in resolving prior and likelihood uncertainty in scene recognition |
| topic | Neuroscience Sensory neuroscience Cognitive neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225009241 |
| work_keys_str_mv | AT kojirohayashi neuralmechanismsinresolvingpriorandlikelihooduncertaintyinscenerecognition AT risakatayama neuralmechanismsinresolvingpriorandlikelihooduncertaintyinscenerecognition AT keisukefujimoto neuralmechanismsinresolvingpriorandlikelihooduncertaintyinscenerecognition AT wakoyoshida neuralmechanismsinresolvingpriorandlikelihooduncertaintyinscenerecognition AT shinishii neuralmechanismsinresolvingpriorandlikelihooduncertaintyinscenerecognition |